Hazelcast IMDG is a clustered, in-memory data-grid that manages application data and distributes processing using in-memory storage and parallel execution for breakthrough application speed and scale. In this Quick Start Guide, learn what an in-memory data grid can be used for, how to do simple query operations with Hazelcast IMDG, what sharing means with Hazelcast, and more. This guide is intended for software architects and developers who are planning or building systems requiring distribute infrastructure for application scalability and performance.
This white paper, written by Java Champion Ben Evans, provides an introduction for architects and developers to Hazelcast®’s distributed computing technology.
Hazelcast Cloud is an enterprise-grade in-memory computing platform deployed and managed by the Hazelcast CloudOps team. The service is powered by Hazelcast IMDG Enterprise HD and leverages widely adopted technologies, such as Docker and Kubernetes, to provide dynamic orchestration and containerization. Hazelcast Cloud supports applications developed in some of the most common languages, including Java, Node.js, Python. Go, and .NET.
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The in-memory speed you count on, with the convenience and scalability of cloud.
Upgrade your Hazelcast IMDG members and clients in a running cluster without interrupting operations.
Overview
Member Rolling Upgrade
Upgrade cluster members without service interruption in embedded mode (no clients) or client-server mode (clients are connected to a cluster).
Client Rolling Upgrade
Upgrade clients individually with no interruptions to other clients, even when working off a central cache.
Supports mixing clients of different languages and versions in one cluster.
Architecture
Upgrade your cluster members without any service interruption and with complete migration of processing and data as each member is upgraded.
Upgrade the clients independent of the server, without service interruption, using Hazelcast Open Client Protocol,which supports six client languages including Java, C++, .NET, Python, Node.js, Go, and Scala.
Resources
Enrichment is a frequent technical use case in stream processing. It is a translation of the traditional star schema into the low-latency continuous processing world: the stream of facts is enriched using slowly changing dimension data. In this webinar you will learn how to do high-performance stream enrichment. We’ll discuss multiple ways of enrichment, explaining the trade-offs. We will feature hands-on examples and live coding using Hazelcast Jet 0.7.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing? In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered.
